Object counters based on a sensor and an analyzer cooperating for determining the number of objects passing a boundary is widely used in different applications.
An object counter is a device which is used to count objects, like for example people entering or leaving a department store, a train station or any other area of interest, livestock leaving or entering an area, products passing on a conveyor belt, or products passing on a conveyor slide, etc. The object counter may for example be used for security purposes but also for generating statistics of objects entering or leaving an area.
An object counter comprises in general terms a sensing part and an analyzing part. The sensing part commonly based on a sensor detecting some feature related to the objects, like for example an image sensor detecting the visible part of the light spectrum for detecting visible features of objects, a Focal Plane Array like for example a microbolometer sensor detecting in the infrared part of the light spectrum for registering the thermal profile of objects or a time-of-flight sensor system creating an image of the distance to objects in an image view.
In the case of the sensing part being a sensor registering features of the objects in an array, e.g., registering data that may be interpreted and analyzed by means of image analyzing tools, then the analyzing part generally is adapted for image analyzing. In most object counters the image analysis is based on object detection algorithms, e.g., in which individual objects are detected, identified, and tracked throughout the area covered by the sensor and then counted as they pass by a predetermined boundary. Several types of object detection algorithms are known to the person skilled in the art.
One problem with the current object detection algorithms is that objects being close together, having similar features, and/or having approximately the same speed are very difficult to detect as separate objects. Situations where these problems are evident are, for instance, when counting objects that are haphazardly outputted onto a conveyer belt as the objects may arrange themselves close together or on top of each other in clusters of varying sizes, when counting people entering or exiting shops or grocery stores, as people often enters in clusters, i.e., enters in groups of two or more walking closely together, and other similar situations. A cluster of objects should be understood as a group of objects grouped close together. The problem occurs because the cluster of objects may be detected as one single object. Many object counters rely on simply counting the detected objects which will result in an underestimation of the number of objects if the objects arrive in clusters and if each cluster is counted as one object.
In some prior art solutions the shape of the detected object is analyzed in order to estimate a more accurate count of the number of objects comprised in a detected object. However, such shape analysis of the detected objects requires a lot of processing power, hence, limiting the use of the method to devices having a lot of spare processing power. Such processing power may not be available in embedded systems or devices having small form factor.
In WO 2009/016614 a process for counting and measuring is described. The process includes capturing of a background image not including any objects to be counted. Then an image is taken when objects are placed in front of the camera. The areas differing from the background image are interpreted as objects and the number of objects may be calculated by multiplying the area of the interpreted objects by a factor or by using a lookup table including values connecting an area to a value indicating the number of objects. This method is simple and effective when it comes to counting stationary objects. However, when moving objects passing a boundary are to be counted, this method is not reliable.